import torch
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops

from torch_geometric.nn.inits import glorot, zeros


class CachedGCNConv(MessagePassing):
    r"""The graph convolutional operator from the `"Semi-supervised
    Classification with Graph Convolutional Networks"
    <https://arxiv.org/abs/1609.02907>`_ paper

    .. math::
        \mathbf{X}^{\prime} = \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
        \mathbf{\hat{D}}^{-1/2} \mathbf{X} \mathbf{\Theta},

    where :math:`\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}` denotes the
    adjacency matrix with inserted self-loops and
    :math:`\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}` its diagonal degree matrix.

    Args:
        in_channels (int): Size of each input sample.
        out_channels (int): Size of each output sample.
        improved (bool, optional): If set to :obj:`True`, the layer computes
            :math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`.
            (default: :obj:`False`)
        cached (bool, optional): If set to :obj:`True`, the layer will cache
            the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
            \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the
            cached version for further executions.
            This parameter should only be set to :obj:`True` in transductive
            learning scenarios. (default: :obj:`False`)
        bias (bool, optional): If set to :obj:`False`, the layer will not learn
            an additive bias. (default: :obj:`True`)
        **kwargs (optional): Additional arguments of
            :class:`torch_geometric.nn.conv.MessagePassing`.
    """

    def __init__(self, in_channels, out_channels,
                 weight=None,
                 bias=None,
                 improved=False,
                 use_bias=True, **kwargs):
        super().__init__(aggr='add', **kwargs)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.improved = improved
        self.cache_dict = {}

        # self.weight = Parameter(torch.Tensor(in_channels, out_channels))
        #
        # if bias:
        #     self.bias = Parameter(torch.Tensor(out_channels))
        # else:
        #     self.register_parameter('bias', None)


        if weight is None:
            self.weight = Parameter(torch.Tensor(in_channels, out_channels).to(torch.float32))
            glorot(self.weight)
        else:
            self.weight = weight
            print("use shared weight")

        if bias is None:
            if use_bias:
                self.bias = Parameter(torch.Tensor(out_channels).to(torch.float32))
            else:
                self.register_parameter('bias', None)
            zeros(self.bias)
        else:
            self.bias = bias
            print("use shared bias")

        # self.reset_parameters()

    # def reset_parameters(self):
    #     glorot(self.weight)
    #     zeros(self.bias)
        # self.cached_result = None
        # self.cached_num_edges = None

    @staticmethod
    def norm(edge_index, num_nodes, edge_weight=None, improved=False,
             dtype=None):
        if edge_weight is None:
            edge_weight = torch.ones((edge_index.size(1), ), dtype=dtype,
                                     device=edge_index.device)

        fill_value = 1 if not improved else 2
        edge_index, edge_weight = add_remaining_self_loops(
            edge_index, edge_weight, fill_value, num_nodes)

        row, col = edge_index
        deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
        deg_inv_sqrt = deg.pow(-0.5)
        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0

        return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]

    def forward(self, x, edge_index, cache_name="default_cache", edge_weight=None):
        """"""

        x = torch.matmul(x, self.weight)
        # print("edge_index is ", edge_index)
        # print("edge_weight is ", edge_weight)
        # print("max of edge_index is ", torch.max(edge_index))
        # print("num_nodes is", x.size(0))
        if not cache_name in self.cache_dict:
            edge_index, norm = self.norm(edge_index, x.size(0), edge_weight,
                                         self.improved, x.dtype)
            self.cache_dict[cache_name] = edge_index, norm
        else:
            edge_index, norm = self.cache_dict[cache_name]


        return self.propagate(edge_index, x=x, norm=norm)

    def message(self, x_j, norm):
        return norm.view(-1, 1) * x_j

    def update(self, aggr_out):
        if self.bias is not None:
            aggr_out = aggr_out + self.bias
        return aggr_out

    def __repr__(self):
        return '{}({}, {})'.format(self.__class__.__name__, self.in_channels, self.out_channels)

